Two recent papers from Anthropic focus on understanding the internal mechanisms of large language models, specifically Claude Haiku 3.5. They introduce an 'AI Microscope' to identify interpretable concepts and their connections to computational processes that generate language. Amid ongoing challenges in interpretation due to the models' opaque decision-making strategies, the AI Microscope explores how neural features can represent significant activities. Researchers also address performance differences by establishing local replacement models that maintain output consistency, shedding light on how models can produce hallucinations and other behaviors.
"To explore the hidden layer of reasoning, Anthropic researchers have developed a novel approach they call the 'AI Microscope', inspired by neuroscience to identify patterns of activity and flows of information."
"Anthropic's AI microscope involves replacing the model under study with a so-called replacement model, where neurons are replaced by sparsely-active features representing interpretable concepts."
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